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February 14, 2026Journal of Dairy Research0 citations

Comparison of BLUPF90IOD3 and MiXBLUP implementations of the single-step model applied to the Polish national dairy cattle evaluation

DSDawid SłomianMJMichalina JakimowiczTSTomasz Suchocki

Key Points

  • This study aims to compare the performance of two software implementations for predicting breeding values in dairy cattle.
  • Compared MiXBLUP and BLUPF90IOD3 software implementations.
  • Utilized phenotypic, genomic, and pedigree data in a single-step G-BLUP model.
  • Tested four core animal sets with data from the Polish national evaluation.
  • Predicted and validated genomic breeding values (GEBVs) across different population subsets.
  • Both software packages showed high correlations (0.89 and 0.97) for predictions.
  • Similar validation performance was observed between the two software implementations.
  • MiXBLUP showed slightly greater consistency across different core animal sets.
  • Ranking of the top 50 bulls was stable in both software implementations.

Abstract

Abstract The integration of phenotypic, genomic and pedigree data into a single-step model for predicting genomically enhanced estimated breeding values (GEBVs) has become crucial for the accurate genetic evaluation of dairy cattle. This study compared two widely used software implementations, MiXBLUP and BLUPF90IOD3, for the prediction of breeding values using the single-step G-BLUP model based on data from the Polish national evaluation for stature. Four core animal sets were tested, which differed in the selection of bulls and cows. The GEBVs were predicted and validated using different subsets of the population. Both software packages resulted in high correlations (0.89 and 0.97) between full and truncated dataset predictions and similar validation performance, with MiXBLUP exhibiting slightly greater consistency across different sets of core animals. The ranking of the top 50 bulls remained stable across the implementations. This study concludes that both software implementations provide comparable GEBV predictions, suggesting that software choice should consider computational efficiency, cost and modeling flexibility, with MiXBLUP offering additional options for GEBV estimation.

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Cite This Study

Słomian et al. (2026) studied this question.

synapsesocial.com/papers/6990112b2ccff479cfe57a03https://doi.org/10.1017/s0022029926102088
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